Device for the logical validation of AI output data (Truth Layer)

The AI wall with a commons architecture validates AI outputs against logical axioms to prevent logical errors, ensuring consistent and trustworthy AI outputs.

DE202026000250U1Active Publication Date: 2026-04-09NIMZ OLIVER
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Patent Information

Authority / Receiving Office
DE · DE
Patent Type
Utility models
Current Assignee / Owner
Filing Date
2026-01-20
Publication Date
2026-04-09

AI Technical Summary

Technical Problem

Current generative AI systems produce stochastic outputs that can lead to content-wise incorrect or logically contradictory results, compromising user cognitive sovereignty.

Method used

A deterministic control instance, implemented as an AI wall with a commons architecture, validates AI outputs against an external database of logical axioms to ensure causal consistency.

Benefits of technology

Ensures only verified, causally consistent information is output, preventing logical errors and maintaining user trust.

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Abstract

System for causal-logical validation of AI output values, comprising an AI module and a logic validator, wherein the logic validator has access to an axiomatic reference database (Commons architecture).
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Description

[0001] Name: System for causal-logical validation of output values ​​of generative AI models using an axiomatic barrier layer.

[0002] Technical field: The invention relates to the safeguarding of artificial intelligence (AI), in particular the technical prevention of logical errors (hallucinations) in language models.

[0003] State of the art: Current generative AI systems operate stochastically (probability-based). This often leads to content-wise incorrect or logically contradictory outputs, which jeopardizes the user's cognitive sovereignty.

[0004] Objective of the invention: Creation of a deterministic control instance that checks AI outputs for their logical statics before displaying them.

[0005] Solution: The invention introduces an AI wall that acts as a barrier layer. At its core is a commons architecture that validates the AI's output against an external database of logical axioms (e.g., the identity principle A = A, causal chains).

[0006] Advantages: The blocking mechanism in case of logic errors ensures that only verified, causally consistent information is output. 2